Next Article in Journal
Diffusion Dynamics with Changing Network Composition
Previous Article in Journal
3D Reconstruction of Coronal Loops by the Principal Component Analysis
Open AccessArticle

A Kernel-Based Calculation of Information on a Metric Space

1
School of Mathematics, Trinity College Dublin, Dublin 2, Ireland
2
Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol BS8 1UB, UK
*
Author to whom correspondence should be addressed.
Entropy 2013, 15(10), 4540-4552; https://doi.org/10.3390/e15104540
Received: 24 July 2013 / Revised: 14 October 2013 / Accepted: 14 October 2013 / Published: 22 October 2013
Kernel density estimation is a technique for approximating probability distributions. Here, it is applied to the calculation of mutual information on a metric space. This is motivated by the problem in neuroscience of calculating the mutual information between stimuli and spiking responses; the space of these responses is a metric space. It is shown that kernel density estimation on a metric space resembles the k-nearest-neighbor approach. This approach is applied to a toy dataset designed to mimic electrophysiological data. View Full-Text
Keywords: mutual information; neuroscience; electrophysiology; metric spaces; kernel density estimation mutual information; neuroscience; electrophysiology; metric spaces; kernel density estimation
Show Figures

Figure 1

MDPI and ACS Style

Tobin, R.J.; Houghton, C.J. A Kernel-Based Calculation of Information on a Metric Space. Entropy 2013, 15, 4540-4552.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop